BIROn - Birkbeck Institutional Research Online

    Evaluating variable length Markov chain models for analysis of user web navigation sessions

    de Moura Borges, J.L.C. and Levene, Mark (2006) Evaluating variable length Markov chain models for analysis of user web navigation sessions. arXiv.

    Full text not available from this repository.

    Abstract

    Markov models have been widely used to represent and analyse user web navigation data. In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable length Markov model to summarise user web navigation sessions up to a given length. While the summarisation ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalise a web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarisation ability.

    Metadata

    Item Type: Other
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 May 2021 19:34
    Last Modified: 25 May 2021 19:34
    URI: https://eprints.bbk.ac.uk/id/eprint/44432

    Statistics

    Downloads
    Activity Overview
    0Downloads
    16Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item